3.8 Indicator reliability

The indicator reliability test (Table 7) was done in order to ensure the quality of variable with formative indicators. This test result can be gained from the significant of weights or indicator weights. All of the formative indicators have met the

requirement of indicator reliability with a p-value less than 0.05 (<0.05) and all

The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social…

DOI: http://dx.doi.org/10.5772/intechopen.84674

Variable with formative indicators will meet the requirements of collinearity, if

the value of variances inflation factor (VIF) is <3.3. KSJ (Welfare) is a latent variable of welfare with four formative indicators, and has the value of variance

instruments are valid [23, 25].

Internal consistency test (Cronbach's alpha of each variable).

inflation factor is 2.527 less than 3.3.

3.9 Collinearity

Table 4.

Table 5.

Table 6.

25

Indicators reliability test.

Composite reliability coefficients.


Table 3. Discriminant validity test.

The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social… DOI: http://dx.doi.org/10.5772/intechopen.84674


#### Table 4.

3.3 Discriminant validity

Protected Areas, National Parks and Sustainable Future

validity are therefore met [23, 25]

means all instruments are reliable [23, 25].

than 0.60 (>0.60), and are thus reliable (Table 5).

3.5 Consistency internal reliability

3.6 Composite reliability

3.7 Cronbach alpha

therefore met [25].

Table 3.

24

Discriminant validity test.

3.8 Indicator reliability

3.4 Reliability test

A discriminant validity test (Table 3) was performed after those for convergent

Reliability test (Table 4) consist of indicator reliability and consistency internal reliability both composite reliability and Cronbach alpha. The reliability test shows that all of the outer loadings are >0.6, and p-value is <0.001 less than 0.05, which

Consistency internal reliability was tested both for composite reliability, and Cronbach alpha. The consistency of internal reliability values in this study also more

The composite reliability coefficients values in this research are more than 0.70 (see Table 5). All variables meet reliability requirements [23]. The value for KLM (forest institution connectedness) is 0.846, ISN (incentive participation program) is 0.967, MDS (social capital) is 0.868, and PAR (public participation) is 0.940.

Internal consistency test (Table 6) can be proved by the exact Cronbach alpha values. The Cronbach alpha are as follows: KLM (X1) is 0.770, ISN (X2) is 0.951, MDS (X3) is 0.809, and PAR (Z1) is 0.914. The criteria for internal consistency are

The indicator reliability test (Table 7) was done in order to ensure the quality of variable with formative indicators. This test result can be gained from the significant of weights or indicator weights. All of the formative indicators have met the

validity. It is to identify the validity of instrument items in a model [27]. The discriminant construct validity test will meet the criteria of the discriminant validity if the square roots of AVE are higher than the variable correlation score. KLM (X1) has a square root of AVE 0.793 is more than its correlation 0.669, 0.281, 0.669. ISN (X2) is 0.938, its correlation scores are 0.669, 0.299, and 0.681. MDS (X3) is 0.755, and its correlation scores are 0.281, 0.299, and 0.304. PAR (Z1) is 0.892, and its correlation scores are 0.669, 0.681, and 0.304. The criteria of discriminant

Indicators reliability test.


### Table 5.

Composite reliability coefficients.


#### Table 6.

Internal consistency test (Cronbach's alpha of each variable).

requirement of indicator reliability with a p-value less than 0.05 (<0.05) and all instruments are valid [23, 25].

#### 3.9 Collinearity

Variable with formative indicators will meet the requirements of collinearity, if the value of variances inflation factor (VIF) is <3.3. KSJ (Welfare) is a latent variable of welfare with four formative indicators, and has the value of variance inflation factor is 2.527 less than 3.3.

## 3.10 Partial least square analysis

Goodness of fit (inner model) can be evaluated based on R-squared, adj. Rsquared, Cronbach alpha, Avg. Var. Ectrac, full collinearity VIF, and Q-squared value (see Table 8). R-squared with high value means the model is good and Rsquared can be used for response variable.

The results of R-squared for the public participation (PAR) is 0.979 which means that the contribution of the variables incentive participation program (ISN), social capital (MDS), and welfare (KSJ) to the effect on public participation (PAR) is 97.9%, and the remaining 2.1% is attributable to another variable outside the research model.

Incentive participation program variable consist of four indicators (incentive participation programs of training, agriculture tools, cash payment, and agriculture land use) are categorized as not good conditions (3.81 < 4.00). The highest outer loading is the incentive participation program of agriculture tools (0.986), mean score (3.97), but it is still reflected not good condition (<4.00). The lowest mean score is the incentive participation program of training (3.67) is reflected as not good condition and effects the level of public participation, especially in developing

The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social…

The social capital variable is consist of five indicators (reciprocity, social norms,

Public participation variable is consist of four indicators (participation in planning program, participation in implementation, participation in benefit-sharing, and participation in monitoring and evaluation) are categorized as not good condition with average score is 3.85 or less than 4.00. The highest outer loading is participation in implementation (0.915) and mean score (3.85). The lowest mean score is participation in planning program (3.68) is reflected as not good condition.

Path coefficients and p values (Table 10) and direct hypothesis (Table 11) that: (H1a) KLM (forest institutions' connectedness) does not have a positive significant effect (0.087) on public participation, with p-value 0.166; (H1b) KSJ (Welfare) mediates the effect of KLM (forest institution connectedness) on public participation (0.552), with p-value <0.001; (H2a) INS (incentive participation program) has shown a positive significant effect (0.196) on public participation (p-value 0.013); (H2b) welfare (KSJ) mediates the effect of ISN (incentive participation program) (0.273) on public participation with p-value <0.001; (H3a) MDS (social capital) has a positive significant effect (0.141) on public participation, with p-value 0.056; (H3b) welfare (KSJ) mediates the effect of MDS (social capital) on public participation (0.177), with p-value 0.023; (H4) welfare (KSJ) has a positive

significant effect (0.782) on public participation with p-value <0.001.

network interaction, level of trust in the community group, and buffer villages group donations) are categorized as not good conditions (3.93 < 4.00). The highest outer loading is social norms (0.819) and means score is (4.21) is reflected as good condition. But the lowest mean score is buffer village group's donation (3.64) is reflected as not good condition and effects the level of public participation. Welfare variable is consist of five indicators (household income, household education, household health, and household supporting facilities) are categorized as not good condition (3.81 < 4.00). The highest outer loading is family income (0.877) and mean score (3.86) is reflected as not good condition and effects the level of public participation (<4.00). The lowest mean score is family supporting facilities (3.73) is reflected as not good condition and effects the level of public

the quality of human resources.

Output latent variable coefficients.

DOI: http://dx.doi.org/10.5772/intechopen.84674

3.12 Path coefficients and P values

participation.

27

Table 8.

Composite reliability value and Cronbach alpha can be used to evaluate research instruments. Based on the output, the composite reliability coefficients are 0.846 for KLM, 0.967 for ISN, 0.868 for MDS, 0.907 for KSJ and 0.940 for PAR. They are more than 0.60 and the Cronbach alpha coefficients are 0.770, 0.951, 0.809, 0.861, and 9.14. All of them are more 0.70 for all variables. Therefore, all variables in this research have met the reliability criteria.

The average variances extracted (AVE) is used to evaluate the discriminant validity, with the criterion that values must be >0.50. The AVE values are as follows: (1) forest institution connectedness (KLM) variable is 0.543; (2) incentive participation program (ISN) variable is 0.880; (2) social capital (MDS) variable is 0.571; (3) welfare (KSJ) variable is 0.713; and (4) public participation (PAR) variable is 0.795. All the variables met the AVE value criterion >0.50 and meet the discriminant validity.

Full collinearity VIFs is a complete collinearity test consisting of vertical and lateral multicollinearity. Lateral collinearity is a collinearity between a predictor latent variable and criteria variables and can be used to test the common method bias. The criterion for the full collinearity test values <3.3. This research has met the full collinearity requirements for all variables; they are 2.228 for KLM, 2.254 for ISN, 1.126 for MDS, and 2.527 for KSJ.

Q-squared is used as a predictive test of the relation between the predictor latent variables and the criterion variables. The Q-squared result can be negative, but the R-squared result must be positive. The estimation result of this output above shows good predictive value; at 0.708 and 0.852, values are more than zero (Table 8).

#### 3.11 Loading factor (outer model)

The outer loading values are used to know indicator's weight of every variable. Indicators with high outer loading values show they are strong variable measures (Table 9). Forest institutions' connectedness variable is consist of five indicators (accountability, transparency, belief-based relationship, forest rules, and information access) are categorized as not good condition (3.86 < 4.00). The highest outer loading is forest rules (0.803) and means score (4.00) is reflected as good condition. But the lowest mean score is accountability (3.71) is reflected as not good condition and effects the level of public participation.


Table 7. Indicator reliability test of indicator weights. The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social… DOI: http://dx.doi.org/10.5772/intechopen.84674


#### Table 8.

3.10 Partial least square analysis

research model.

discriminant validity.

squared can be used for response variable.

Protected Areas, National Parks and Sustainable Future

research have met the reliability criteria.

ISN, 1.126 for MDS, and 2.527 for KSJ.

3.11 Loading factor (outer model)

and effects the level of public participation.

Indicator reliability test of indicator weights.

Table 7.

26

Goodness of fit (inner model) can be evaluated based on R-squared, adj. Rsquared, Cronbach alpha, Avg. Var. Ectrac, full collinearity VIF, and Q-squared value (see Table 8). R-squared with high value means the model is good and R-

The results of R-squared for the public participation (PAR) is 0.979 which means that the contribution of the variables incentive participation program (ISN), social capital (MDS), and welfare (KSJ) to the effect on public participation (PAR) is 97.9%, and the remaining 2.1% is attributable to another variable outside the

Composite reliability value and Cronbach alpha can be used to evaluate research instruments. Based on the output, the composite reliability coefficients are 0.846 for KLM, 0.967 for ISN, 0.868 for MDS, 0.907 for KSJ and 0.940 for PAR. They are more than 0.60 and the Cronbach alpha coefficients are 0.770, 0.951, 0.809, 0.861, and 9.14. All of them are more 0.70 for all variables. Therefore, all variables in this

The average variances extracted (AVE) is used to evaluate the discriminant validity, with the criterion that values must be >0.50. The AVE values are as follows: (1) forest institution connectedness (KLM) variable is 0.543; (2) incentive participation program (ISN) variable is 0.880; (2) social capital (MDS) variable is 0.571; (3) welfare (KSJ) variable is 0.713; and (4) public participation (PAR) variable is 0.795. All the variables met the AVE value criterion >0.50 and meet the

Full collinearity VIFs is a complete collinearity test consisting of vertical and lateral multicollinearity. Lateral collinearity is a collinearity between a predictor latent variable and criteria variables and can be used to test the common method bias. The criterion for the full collinearity test values <3.3. This research has met the full collinearity requirements for all variables; they are 2.228 for KLM, 2.254 for

Q-squared is used as a predictive test of the relation between the predictor latent variables and the criterion variables. The Q-squared result can be negative, but the R-squared result must be positive. The estimation result of this output above shows good predictive value; at 0.708 and 0.852, values are more than zero (Table 8).

The outer loading values are used to know indicator's weight of every variable. Indicators with high outer loading values show they are strong variable measures (Table 9). Forest institutions' connectedness variable is consist of five indicators (accountability, transparency, belief-based relationship, forest rules, and information access) are categorized as not good condition (3.86 < 4.00). The highest outer loading is forest rules (0.803) and means score (4.00) is reflected as good condition. But the lowest mean score is accountability (3.71) is reflected as not good condition Output latent variable coefficients.

Incentive participation program variable consist of four indicators (incentive participation programs of training, agriculture tools, cash payment, and agriculture land use) are categorized as not good conditions (3.81 < 4.00). The highest outer loading is the incentive participation program of agriculture tools (0.986), mean score (3.97), but it is still reflected not good condition (<4.00). The lowest mean score is the incentive participation program of training (3.67) is reflected as not good condition and effects the level of public participation, especially in developing the quality of human resources.

The social capital variable is consist of five indicators (reciprocity, social norms, network interaction, level of trust in the community group, and buffer villages group donations) are categorized as not good conditions (3.93 < 4.00). The highest outer loading is social norms (0.819) and means score is (4.21) is reflected as good condition. But the lowest mean score is buffer village group's donation (3.64) is reflected as not good condition and effects the level of public participation.

Welfare variable is consist of five indicators (household income, household education, household health, and household supporting facilities) are categorized as not good condition (3.81 < 4.00). The highest outer loading is family income (0.877) and mean score (3.86) is reflected as not good condition and effects the level of public participation (<4.00). The lowest mean score is family supporting facilities (3.73) is reflected as not good condition and effects the level of public participation.

Public participation variable is consist of four indicators (participation in planning program, participation in implementation, participation in benefit-sharing, and participation in monitoring and evaluation) are categorized as not good condition with average score is 3.85 or less than 4.00. The highest outer loading is participation in implementation (0.915) and mean score (3.85). The lowest mean score is participation in planning program (3.68) is reflected as not good condition.

#### 3.12 Path coefficients and P values

Path coefficients and p values (Table 10) and direct hypothesis (Table 11) that: (H1a) KLM (forest institutions' connectedness) does not have a positive significant effect (0.087) on public participation, with p-value 0.166; (H1b) KSJ (Welfare) mediates the effect of KLM (forest institution connectedness) on public participation (0.552), with p-value <0.001; (H2a) INS (incentive participation program) has shown a positive significant effect (0.196) on public participation (p-value 0.013); (H2b) welfare (KSJ) mediates the effect of ISN (incentive participation program) (0.273) on public participation with p-value <0.001; (H3a) MDS (social capital) has a positive significant effect (0.141) on public participation, with p-value 0.056; (H3b) welfare (KSJ) mediates the effect of MDS (social capital) on public participation (0.177), with p-value 0.023; (H4) welfare (KSJ) has a positive significant effect (0.782) on public participation with p-value <0.001.


(0.013 < 0.05), H2a is accepted. The results support the theory of incentive participation programs of Robbin [28], Adhikari et al. [12], Djamhuri [13], and Kaseya

The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social…

Social capital gives significant positive effect on public participation (0.1401), with p-value 0.056. Because p-value 0.056 is less than 0.05 (0.056 < 0.5), H3a is accepted. The test results support the theory of social capita [29], as well as

Path coefficient indirect effect (Table 12) shows that welfare mediates the effect of forest institution on public participation (0.552), with p-value <0.001. Because p-value <0.001 is less than 0.05 (<0.001 < 0.5), hypothesis H1b is accepted. The test results provide empirical support for the work of Hans-Jurgen [10] and Akib

Welfare mediates incentive participation program on public participation (0.273), with p-value < 0.001, less than 0.05 (<0.001 < 0.5). Hypothesis H2b is thus accepted. The test results provide empirical support for the work of Rahut et al.

Social capital by the mediation of welfare has a positive significant effect on public participation (0.177), with p-value 0.023, less than 0.05 (0.023 < 0.5). Hypothesis H3b is therefore accepted. The results are related to the social capital theory [20]. In addition, Fukuyama [30] added that the social capital and the level of welfare are closely related in a community or nation [29]. This result provides empirical support for the research of Grootaet [20], Narayan and Pritchett [21]. Welfare contributes significant positive effect on public participation by 0.782 on public participation, with p-value <0.001. Because p-value <0.001 is less than 0.05 (<0.001 < 0.5), hypothesis H4 is accepted. This test provides empirical sup-

port for the research of Rahut et al. [15] and Akamani and Hall [22].

To test mediation effect, this research uses Baron and Kenny's causal-step approach. Baron and Kenny [26] using causal step approach which has four mediation effects, they are: (a) first step, directional hypothesis if the results are significant/positive; (b) second step, the indirect hypothesis was tested whether it is significant/positive; (c) third step, test mediation effects using VAF (Variance Accounted For) with the criteria: VAF value >80% means full mediation,

20% ≤ VAF ≤80% means partial mediation; and VAF < 20% means no mediation.

Figure 1 shows that all of the direct effects are significant/positive because the p-values are less than 0.05. Then the indirect effects (mediation variables) are

The mediation effect is significant/positive if p-value indirect effect is less

supporting the empirical research of Sara [18] and Sharpe [19].

[15], William and Ayuk [16], Das and Sarker [17].

and Kihonge [14].

et al. [11].

3.14 Indirect effect hypothesis

DOI: http://dx.doi.org/10.5772/intechopen.84674

3.15 Mediation effect analysis

included, as shown in Figure 2.

than 0.05 [25].

Table 11. Direct hypothesis.

29

Table 9. Outer loading value of variable.


Table 10. Path coefficients and P values.

#### 3.13 Directional hypothesis

Forest institutions' connectedness does not have a positive significant effect (0.087) on public participation, with p-value 0.166. Because p-value 0.166 is more than 0.05 (0.166 > 0.05), H1a is not accepted. This test result does not provide empirical support for the findings of Baynes et al. [7], Muro and Namusonge [8], and Lise [9].

The incentive participation program has a positive significant effect (0.196) on public participation, with p-value 0.013. Because p-value 0.013 is less than 0.5

The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social… DOI: http://dx.doi.org/10.5772/intechopen.84674

(0.013 < 0.05), H2a is accepted. The results support the theory of incentive participation programs of Robbin [28], Adhikari et al. [12], Djamhuri [13], and Kaseya and Kihonge [14].

Social capital gives significant positive effect on public participation (0.1401), with p-value 0.056. Because p-value 0.056 is less than 0.05 (0.056 < 0.5), H3a is accepted. The test results support the theory of social capita [29], as well as supporting the empirical research of Sara [18] and Sharpe [19].

#### 3.14 Indirect effect hypothesis

Path coefficient indirect effect (Table 12) shows that welfare mediates the effect of forest institution on public participation (0.552), with p-value <0.001. Because p-value <0.001 is less than 0.05 (<0.001 < 0.5), hypothesis H1b is accepted. The test results provide empirical support for the work of Hans-Jurgen [10] and Akib et al. [11].

Welfare mediates incentive participation program on public participation (0.273), with p-value < 0.001, less than 0.05 (<0.001 < 0.5). Hypothesis H2b is thus accepted. The test results provide empirical support for the work of Rahut et al. [15], William and Ayuk [16], Das and Sarker [17].

Social capital by the mediation of welfare has a positive significant effect on public participation (0.177), with p-value 0.023, less than 0.05 (0.023 < 0.5). Hypothesis H3b is therefore accepted. The results are related to the social capital theory [20]. In addition, Fukuyama [30] added that the social capital and the level of welfare are closely related in a community or nation [29]. This result provides empirical support for the research of Grootaet [20], Narayan and Pritchett [21].

Welfare contributes significant positive effect on public participation by 0.782 on public participation, with p-value <0.001. Because p-value <0.001 is less than 0.05 (<0.001 < 0.5), hypothesis H4 is accepted. This test provides empirical support for the research of Rahut et al. [15] and Akamani and Hall [22].

#### 3.15 Mediation effect analysis

To test mediation effect, this research uses Baron and Kenny's causal-step approach. Baron and Kenny [26] using causal step approach which has four mediation effects, they are: (a) first step, directional hypothesis if the results are significant/positive; (b) second step, the indirect hypothesis was tested whether it is significant/positive; (c) third step, test mediation effects using VAF (Variance Accounted For) with the criteria: VAF value >80% means full mediation, 20% ≤ VAF ≤80% means partial mediation; and VAF < 20% means no mediation. The mediation effect is significant/positive if p-value indirect effect is less than 0.05 [25].

Figure 1 shows that all of the direct effects are significant/positive because the p-values are less than 0.05. Then the indirect effects (mediation variables) are included, as shown in Figure 2.


Table 11. Direct hypothesis.

3.13 Directional hypothesis

Path coefficients and P values.

and Lise [9].

28

Table 10.

Table 9.

Outer loading value of variable.

Protected Areas, National Parks and Sustainable Future

Forest institutions' connectedness does not have a positive significant effect (0.087) on public participation, with p-value 0.166. Because p-value 0.166 is more than 0.05 (0.166 > 0.05), H1a is not accepted. This test result does not provide empirical support for the findings of Baynes et al. [7], Muro and Namusonge [8],

The incentive participation program has a positive significant effect (0.196) on public participation, with p-value 0.013. Because p-value 0.013 is less than 0.5

VAF = (0.552 0.782)/(0.552 0.782 + 0.087).

VAF = (0.273 0.782)/(0.273 0.782 + 0.196).

significant and positive, with p-value 0.023 (<0.05)

VAF = (0.177 0.782)/(0.177 0.782 + 0.141).

This means that welfare mediates the effect of forest institutions' connectedness on

The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social…

This means that welfare mediates incentive participation program's effect on public

3. Social capital's effect on public participation in the mediation of welfare is

This means welfare mediates social capital's effect on public participation as a

(Tables 11 and 12), for all variables can be summarized in Table 13.

3.16 Analysis of public participation and welfare as mediator of forest

Based on the descriptive analysis, both direct and indirect hypothesis results

The analysis of public participation in this research is based on the characteristics of five buffer villages. They are Wonorejo, Sumber Waru, Sumber Anyar, Watu

The buffer villages have potential to be developed into bigger villages. Management of regions is required in order to avoid disturbing the forest conservation in

The contribution of nontimber forest product (NTFP) to family income is

around 19.79% up and 61.44% of their total annual income (Table 14).

mediation of welfare is significant and positive, with p-value <0.001 (<0.05)

2. Incentive participation program's effect on public participation in the

VAF = 0.431/0.518. VAF = 0.832. VAF = 83.2%.

VAF = 0.213/0.409. VAF = 0.520. VAF = 52.0%.

VAF = 0.138/0.279. VAF = 0.494. VAF = 49.4%.

partial mediation.

management

Kebo and Bajul Mati.

Baluran National Park.

Path coefficient indirect effect.

Table 12.

31

participation as a partial mediation.

public participation as a full mediation.

DOI: http://dx.doi.org/10.5772/intechopen.84674

Figure 2. Indirect effect including mediation.

Figure 2 shows all of the indirect effects are significant/positive because pvalues are less than 0.05. The third step is to test mediation effect by using the VAF formula. The formula of VAF = (p12 p23)/(p12 p23 + p13). The results of the mediation test using the VAF method are as follows:

1. Forest institutions' connectedness effect on public participation in the mediation of welfare is significant and positive with p-value <0.001 (<0.05) The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social… DOI: http://dx.doi.org/10.5772/intechopen.84674

VAF = (0.552 0.782)/(0.552 0.782 + 0.087). VAF = 0.431/0.518. VAF = 0.832. VAF = 83.2%.

This means that welfare mediates the effect of forest institutions' connectedness on public participation as a full mediation.

2. Incentive participation program's effect on public participation in the mediation of welfare is significant and positive, with p-value <0.001 (<0.05)

VAF = (0.273 0.782)/(0.273 0.782 + 0.196). VAF = 0.213/0.409. VAF = 0.520. VAF = 52.0%.

This means that welfare mediates incentive participation program's effect on public participation as a partial mediation.

3. Social capital's effect on public participation in the mediation of welfare is significant and positive, with p-value 0.023 (<0.05)

VAF = (0.177 0.782)/(0.177 0.782 + 0.141). VAF = 0.138/0.279. VAF = 0.494. VAF = 49.4%.

This means welfare mediates social capital's effect on public participation as a partial mediation.

Based on the descriptive analysis, both direct and indirect hypothesis results (Tables 11 and 12), for all variables can be summarized in Table 13.

### 3.16 Analysis of public participation and welfare as mediator of forest management

The analysis of public participation in this research is based on the characteristics of five buffer villages. They are Wonorejo, Sumber Waru, Sumber Anyar, Watu Kebo and Bajul Mati.

The buffer villages have potential to be developed into bigger villages. Management of regions is required in order to avoid disturbing the forest conservation in Baluran National Park.

The contribution of nontimber forest product (NTFP) to family income is around 19.79% up and 61.44% of their total annual income (Table 14).


Table 12. Path coefficient indirect effect.

Figure 2 shows all of the indirect effects are significant/positive because pvalues are less than 0.05. The third step is to test mediation effect by using the VAF formula. The formula of VAF = (p12 p23)/(p12 p23 + p13). The results of the

1. Forest institutions' connectedness effect on public participation in the

mediation of welfare is significant and positive with p-value <0.001 (<0.05)

mediation test using the VAF method are as follows:

Figure 1.

Figure 2.

30

Indirect effect including mediation.

Direct effect without including mediation.

Protected Areas, National Parks and Sustainable Future


3.19 Public participation in benefit sharing

DOI: http://dx.doi.org/10.5772/intechopen.84674

3.20 Public participation in evaluation and monitoring

medium level, and <17 low-level participation [32].

Public participation in implementing program.

Public participation of benefit sharing.

Public participation in evaluation and monitoring.

22.4% have participated in the monitoring of forest conservation.

value 17.1% (Table 17).

National Park

Table 16.

Table 17.

Table 18.

33

The members of the Forestry Community Training Center have participated in the benefit-sharing from forestry management to increase their family's income with value 21.8%, and have participated in forest conservation management with

The Effect of Forest Institution Connectedness, Incentive Participation Program, and Social…

Table 18 shows that 17.6% of members of Forestry Community Training Center has participated in the evaluation of Baluran Forest in Baluran National Park, and

3.21 Classification of public participation in forestry management in Baluran

According to Cohen [32], the level of public participation is high when people involved in four stages of the management process. They are (1) program planning participation; (2) actuating participation; (3) benefit-sharing participation; and (4) evaluation and monitoring participation. Scores > 21 indicate high level, 17–21

Table 13. Hypothesis test results, summary of direct and indirect effect.

This research also empirically supports research from Baluran [5] and Syafi'i [31]. The total income of user's forest product in buffer villages of Baluran National Park is about Rp. 100,900,000 a year.
